RL10 engine
Residual development over iterations
The discrepancy here is a sort of compounding effect of the pressure drop rhtough the cooling channels being underestimated. When pressure drop rises over cooling channels, higher pressure rise is needed across pump, which again means higher pressure drop across turbine. I decided to leave it in because I do not actually know exactly why the pressure drop in the channels is lower than expected.
Unknown why the reference data has a lower temperature rise than Pyskyfire. If you look at the temperature rise comparison in the regenerative cooling results they are not this excessive.
The discrepancies all over the board is exacerbated by the fact that I am not correcting for ideal vs delivered Isp. This lowers mass flow which has an effect on the whole system. When better Isp estimates are made in the future, these predictions will improve.
I can't figure out why the scaling is bad. I plan on rewriting the engine network visualiser anyways so I will leave this as is for now.
Input Parameters
ParameterValue
p_c3275010
MR5.05
AR_c1.5
F73182
p_e3770
A_e0.708
p_tank_fu190800
p_tank_ox292000
L_star0.95
eps56.1
cea_fuH2 (100%)
cea_oxO2(L) (100%)
coolant_fuHydrogen (100%)
coolprop_fuhydrogen
coolprop_oxoxygen
T_gas_fu_in200
T_gas_ox_in100
theta_conv25
r_c0.123
R_1f1.5
R_2f3
R_3f0.5
length_fraction0.713
n_fu31537
n_ox12615
wall_thickness0.00031
roughness_height1.17e-06
ht_up0.0032
ht_lo0.00134
pn_up0.7
pn_lo-5
eta_pump_fu0.581
eta_pump_ox0.642
stage1_fraction0.5
eta_turbine_fu0.735
eta_stage1_stage2_fu0.95
eta_pump_regen_fu0.95
eta_regen_turbine_fu0.98
eta_turbine_injector_fu0.94
eta_fu_injector0.88
eta_pump_regen_ox0.95
eta_regen_turbine_ox0.95
eta_pump_injector_ox0.88
eta_ox_injector0.88
zeta_stage1_recirc0.007
zeta_stage2_recirc0.00294
zeta_stage2_gearbox0.00294
zeta_turbine_bypass0.00427
zeta_pump_ox_recirc0.0005
T_tank_ox97
T_tank_fu22.7
rho_ox_tank1.08e+03
rho_fu_tank68
thrust_chamber<pyskyfire.regen.thrust_chamber.ThrustChamber object at 0x00000225A8CA5010>
Optimal Values
ParametersValue
MR5.05
p_c3275010
T_c3.3e+03
F73182
eps56.1
L_star0.95
c_star2.39e+03
p_amb100000
Isp_ideal_amb451
Isp_vac468
Isp_amb50.9
Isp_SL45.4
CF_vac1.92
CF_amb0.209
CF_SL0.186
mdot15.9
mdot_fu2.63
mdot_ox13.3
t_stay0.000975
A_t0.0116
A_e0.652
r_t0.0608
r_e0.456
V_c0.011
npts15
This is the report outlining the Pyskyfire simulation of the regenerative cooling of the RL10 engine. Reference data is largely collected from NASA, Binder, 1997, RL10 Modelling Project.
RL10 engine contour
Comparison between simulations presented in NASA, Binder 1997, and Pyskyfire.
Hot wall and cold wall temperature predicted by Pyskyfire.
This thermal boundary layer function is a little experimental. It's based on the 1/7 power law, which is only valid for prandtl numbers neat unity. So currently this is just a nice visual
More granular simulation is possible, but have very minor effects on the results. At this resolution you get good results with very small computational overhead.